The Assignee Prediction Dataset consists of error stack traces and annotations to them. All annotations were collected from the IntelliJ IDEA Community using the "git blame" command. The dataset is anonymized, each entity is encoded with a unique identifier, all the temproal data has been shifted by a fixed timestamp. Please refer to the README for details
This data set will be released as part of the following publication. "Root cause prediction based on...
Many defect prediction techniques have been proposed. While they often take the author of the code i...
Finding defects in proposed changes is one of the biggest motivations and expected outcomes of code ...
This PDF presents confidence intervals for RQs 2 and 3 of the paper "DAPSTEP: Deep Assignee Predicti...
This is the assignment specification and artifacts related to the paper "Assessing the Students' Und...
ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction This archive contains the ApacheJIT d...
Data and source code used to experiment on the publication: Explainable Software Defect Prediction: ...
Dataset and source code for paper “Software Visualization and Deep Transfer Learning for Effective S...
Dataset used for paper "Issues-Driven Features for Software Fault Prediction". The dataset...
We have meticulously prepared this comprehensive replication package to facilitate further investiga...
This dataset is about a systematic review of unsupervised learning techniques for software defect pr...
This repository contains the artifacts of BEEP. The files prediction_CoCoNut.csv, prediction_Man...
This dataset was created using the downloadable defect tickets from the Trac website and also the so...
ContextDefect prediction can help at prioritizing testing tasks by, for instance, ranking a list of ...
Data and source code used to experiment on the publication: An Empirical Study on the Performance of...
This data set will be released as part of the following publication. "Root cause prediction based on...
Many defect prediction techniques have been proposed. While they often take the author of the code i...
Finding defects in proposed changes is one of the biggest motivations and expected outcomes of code ...
This PDF presents confidence intervals for RQs 2 and 3 of the paper "DAPSTEP: Deep Assignee Predicti...
This is the assignment specification and artifacts related to the paper "Assessing the Students' Und...
ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction This archive contains the ApacheJIT d...
Data and source code used to experiment on the publication: Explainable Software Defect Prediction: ...
Dataset and source code for paper “Software Visualization and Deep Transfer Learning for Effective S...
Dataset used for paper "Issues-Driven Features for Software Fault Prediction". The dataset...
We have meticulously prepared this comprehensive replication package to facilitate further investiga...
This dataset is about a systematic review of unsupervised learning techniques for software defect pr...
This repository contains the artifacts of BEEP. The files prediction_CoCoNut.csv, prediction_Man...
This dataset was created using the downloadable defect tickets from the Trac website and also the so...
ContextDefect prediction can help at prioritizing testing tasks by, for instance, ranking a list of ...
Data and source code used to experiment on the publication: An Empirical Study on the Performance of...
This data set will be released as part of the following publication. "Root cause prediction based on...
Many defect prediction techniques have been proposed. While they often take the author of the code i...
Finding defects in proposed changes is one of the biggest motivations and expected outcomes of code ...